WO2001042981A2 - Systeme de recherche et de recuperation de donnees en langage naturel en anglais - Google Patents

Systeme de recherche et de recuperation de donnees en langage naturel en anglais Download PDF

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Publication number
WO2001042981A2
WO2001042981A2 PCT/IB2000/002009 IB0002009W WO0142981A2 WO 2001042981 A2 WO2001042981 A2 WO 2001042981A2 IB 0002009 W IB0002009 W IB 0002009W WO 0142981 A2 WO0142981 A2 WO 0142981A2
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WO
WIPO (PCT)
Prior art keywords
word
new
vector
elementat
temp
Prior art date
Application number
PCT/IB2000/002009
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English (en)
Other versions
WO2001042981A3 (fr
Inventor
Victor Lee
Chris Semotok
Otman Basir
Fakhri Karray
Original Assignee
Qjunction Technology, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Qjunction Technology, Inc. filed Critical Qjunction Technology, Inc.
Priority to AU22128/01A priority Critical patent/AU2212801A/en
Publication of WO2001042981A2 publication Critical patent/WO2001042981A2/fr
Publication of WO2001042981A3 publication Critical patent/WO2001042981A3/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3344—Query execution using natural language analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/3332—Query translation
    • G06F16/3334—Selection or weighting of terms from queries, including natural language queries

Definitions

  • the present invention relates generally to the field of computer searching and retrieval, and more particularly to the field of computer searching and retrieval using natural English language input into the search system.
  • Search and retrieval systems using natural English language input are known in this art. These systems, however, are typically very complex, cumbersome, and costly to implement. Thus, the applicability of these systems to general search and retrieval tasks has been limited. More specifically, these known search and retrieval systems have had very little penetration into the Internet space because of these disadvantages. The known systems do not have a less complex, streamlined, and cost effective search and retrieval system and method that process natural English language inputs.
  • a computer-implemented method and system for searching and retrieving using natural language.
  • the method and system receive a text string having words. At least one of the words is identified as a topic word. Remaining words are classified either as a prefix description or a postfix description.
  • a data store is searched based upon the identified topic word, prefix description, and postfix description. Results from the searching are scored based upon occurrence of the identified topic word, prefix description, and postfix description in the results.
  • FIG. 1 is a flow chart of the preferred natural English language search and retrieval methodology according to the present invention
  • FIG. 2 is a block diagram depicting the computer-implemented components of the present invention.
  • FIG. 1 sets forth a flow chart 10 of the preferred search and retrieval methodology of the present invention.
  • the method begins at step 12, where the user of the system inputs an English sentence or keywords in the form of a text string.
  • the first stage of the system 14 then extracts words from the text string by using spaces as delimiters. Each word is then found in a dictionary 18 to obtain its properties. If the word is not found in the dictionary 18 it is assumed to be a noun.
  • the dictionary 18 contains over 50,000 words with each word associated with one or more properties. These part of speech properties include noun, adjective, adverb, verb, conjunction, determiner (e.g., an article, and preposition).
  • the extracted includes noun, adjective, adverb, verb, conjunction, determiner (e.g., an article, and preposition).
  • the next stage 16 of the system determines a single property for each
  • the rule schema 22 uses the word in question as a pivot and examines the properties of the word before and the properties of the word after the word being analyzed. A decision can only be made when the word before and/or the word after has a single property. If the pivot word's properties cannot be determined because the word before and after has multiple properties, the algorithm proceeds to the next word as the pivot. This process is repeated twice to find a single property for each word. If the rule schema 22 cannot find a single property for a word the default is the first property. The last word of the text string is forced to be a noun.
  • the last stage 26 of the system is an interpreter that cleaves the input sentence into phrases based upon the singular properties of the words as identified in step 16.
  • the delimiter of each phrase is a conjunction, preposition or a comma.
  • the last noun of the first phrase is taken to be the topic (TP).
  • the nouns and adjectives before the topic in the first phrase is termed the Prefix Description (Pre).
  • the nouns and adjectives contained in the following phrases are termed the Postfix Description (Post).
  • Post Postfix Description
  • the topic, Prefix Description and N Postfix Description(s) are stored 28 for use in the search stages 30-36.
  • the input into the search stages 30-36 include a topic containing a single word, a prefix description containing a collection a words, and a postfix description containing a collection a words.
  • the system feeds one or more permutations of TP, Pre and Posts into one or more data miner applications.
  • the data miner applications use data miner domain information 32 in order to apply the search permutations to various Internet domains.
  • Each of the data miner applications then returns its top M search results for the particular Internet domain searched.
  • the system provides the ability to customize the search and retrieval process by specifying what domains to search, and hence what data miners to execute.
  • All of the M search results from the selected data miners are then combined and scored based on the occurrence of TP, Pre, and Posts within the search results at step 34.
  • the score is calculated by the occurrence of each word contained in the topic, prefix and postfix descriptions. Additional points are give if an exact match is made using the same order of words found in the
  • appendices A-G Attached to this application as appendices A-G are the Java source code files that reflect the preferred embodiment of the methodology depicted in FIG. 1. These appendices include: (A) Parser module (which extracts words and find properties); (B) Words Manipulator module (which cleaves sentences into phrases, and associated files); (C) One Subject data structure; (D) One Word data structure; (E) Word Grouping List data structure; (F) Word List data structure; and (G) Filter module (which ranks results according to topic, prefix description, postfix descriptions).
  • FIG. 2 describes the Java source code modules set forth in Appendices (A) - (G).
  • the Parser module 50 receives a user input text string 52.
  • the Parser module 50 reads in dictionary 18 that in this example contains 50,000 words and their associated property codes.
  • the Parser module 50 takes the user input text string 52 and tokenizes it into a data structure using spaces as delimiters.
  • the Parser module 50 uses a binary search algorithm to find each word in the dictionary 18 and determine its
  • Property codes include noun, adjective, adverb, verb, conjunction, determiner, and preposition. If the word is not found in the dictionary 18 it is assumed to be a noun.
  • the Parser module 50 uses the properties rules base 22 to determine a single property code for each word.
  • the rule schema uses the word in question as a
  • pivot examines the properties of the word before and the properties of the word after. The decision is made when the word before and/or the word after has a single property. If the pivot word's properties cannot be determined because the word before and after has multiple properties the algorithm proceeds to the next word as the pivot. The process is repeated twice to find a single property for each word. If the rule schema cannot find a single property for a word the default is the first property. Moreover, the last word of the text string is forced to be a noun.
  • the Words Manipulator module 54 takes each set of words and property codes and places it into the One Word data structure 56. Each group
  • One Word data structure 56 is then cleaved using conjunctions, prepositions, and commas as delimiters into phrases that are stored in the Word List data structure 58. Each entry in the Word List data structure 58 is added to the Word Grouping List data structure 60.
  • the Word Grouping List data structure 60 is decomposed into the One Subject data structure 62 containing topic, prefix description, and postfix descriptions.
  • the last noun of the first phrase of the Word List data structure 58 is taken to be the topic.
  • Nouns and adjectives before the topic in the first phrase of the Word Grouping List data structure 60 form the prefix description.
  • Nouns and adjectives contained in the following phrases in the Word Grouping List data structure 60 are taken as the postfix description.
  • the One Word data structure 56 contains a word and its property code.
  • the Word List data structure 58 contains a phrase of nouns and adjectives.
  • the Word Grouping List data structure 60 contains a group of phrases.
  • the One Subject data structure 62 contains topic, prefix description, postfix descriptions.
  • the Filter module 64 generates permutations of topic, prefix and postfix descriptions.
  • the data miner domain information 32 which may include Internet information uses the permutations to search a domain and return the top results. Results are ranked according to topic, prefix description, postfix descriptions. Points are scored highest for exact matches.
  • a Topic match is scored high, then prefix description and the least points are given to a postfix description match.
  • the ranked best search results 66 are returned to the user.
  • temp compareTof 0
  • temp b ⁇ narySearch(Words, sentence elementAt( ⁇ ) toStnngO toLowerCase(), Codes), coding addElement(temp t ⁇ m()),
  • ⁇ tok new St ⁇ ngToken ⁇ zer(coding.elementAt( ⁇ ).toS r ⁇ ng(), ","); coding. setElementAt( ⁇ ew String(tok.nextToken()), ⁇ );
  • WordList wordList new WordL ⁇ st()
  • Vector list new Vector()
  • groupinglist new WordGroup ⁇ ngL ⁇ st()
  • ⁇ word new Str ⁇ ng(subject getL ⁇ st() elementAt(j) toStnngO), if ( ⁇ sMoney(word))
  • ⁇ ⁇ ⁇ queryStnng new OneSubject(ma ⁇ nSubject, precede, description), return queryStnng,
  • WordGroupList new Vector(), ⁇ public void addGroup(OneSubject subject) ⁇ WordGroupList. addElement(subject);
  • frontText new Stringf
  • frontText new Str ⁇ ng(frontText + " " + prec.elementAt(j).toStr ⁇ ng() toLowerCase())
  • DPOINTS PPOINTS
  • Object tempPoint points. elementAt(i); points. setElementAt(po ⁇ nts.elementAt(j), i), points. setElementAt(tempPoint, j);

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

L'invention concerne un procédé et un système informatique pour rechercher et récupérer des données en utilisant un langage naturel. Le système et le procédé reçoivent une chaîne de textes comportant des mots (12), au moins un des mots étant identifié comme un mot thématique (16). Les mots restants sont classés sous forme d'une description soit de préfixes soit de postfixes (16). On fait des recherches dans une banque de données (32) sur la base du mot thématique identifié, de la description de préfixes ou de la description de postfixes (30). Les résultats de la recherche sont classés en fonction de la fréquence des mots thématiques identifiés, de la description de préfixes ou de la description de postfixes dans les résultats (34).
PCT/IB2000/002009 1999-12-07 2000-12-06 Systeme de recherche et de recuperation de donnees en langage naturel en anglais WO2001042981A2 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU22128/01A AU2212801A (en) 1999-12-07 2000-12-06 Natural english language search and retrieval system and method

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US16941499P 1999-12-07 1999-12-07
US60/169,414 1999-12-07

Publications (2)

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WO2001042981A2 true WO2001042981A2 (fr) 2001-06-14
WO2001042981A3 WO2001042981A3 (fr) 2003-12-24

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US (1) US20010044720A1 (fr)
AU (1) AU2212801A (fr)
WO (1) WO2001042981A2 (fr)

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US10884585B2 (en) 2006-06-22 2021-01-05 Rohit Chandra User widget displaying portions of content
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US11301532B2 (en) 2006-06-22 2022-04-12 Rohit Chandra Searching for user selected portions of content
US11763344B2 (en) 2006-06-22 2023-09-19 Rohit Chandra SaaS for content curation without a browser add-on
US8910060B2 (en) * 2006-06-22 2014-12-09 Rohit Chandra Method and apparatus for highlighting a portion of an internet document for collaboration and subsequent retrieval
US10866713B2 (en) 2006-06-22 2020-12-15 Rohit Chandra Highlighting on a personal digital assistant, mobile handset, eBook, or handheld device
US9292617B2 (en) 2013-03-14 2016-03-22 Rohit Chandra Method and apparatus for enabling content portion selection services for visitors to web pages
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US11853374B2 (en) 2006-06-22 2023-12-26 Rohit Chandra Directly, automatically embedding a content portion
US10289294B2 (en) 2006-06-22 2019-05-14 Rohit Chandra Content selection widget for visitors of web pages
US8661031B2 (en) * 2006-06-23 2014-02-25 Rohit Chandra Method and apparatus for determining the significance and relevance of a web page, or a portion thereof
US10909197B2 (en) 2006-06-22 2021-02-02 Rohit Chandra Curation rank: content portion search
US11429685B2 (en) 2006-06-22 2022-08-30 Rohit Chandra Sharing only a part of a web page—the part selected by a user
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US20010044720A1 (en) 2001-11-22
WO2001042981A3 (fr) 2003-12-24
AU2212801A (en) 2001-06-18

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